Case studies

How engagements actually go.

Three representative engagements, told honestly — challenge, work, outcome. These are anonymised composites drawn from real work: identifying details have been changed and figures rounded, but every pattern described here is one we have delivered.

Finance Anonymised example

Cutting loan first-review time by ~60% with a document agent.

The challenge

A mid-market lender’s analysts spent hours per application re-keying data from PDFs, spotting missing documents late, and duplicating checks across systems. Backlogs grew; decisions slowed.

What we did

Across four one-week sprints we shipped a document agent that reads each application pack, extracts and cross-checks key fields, flags gaps and inconsistencies, and drafts the first-review summary — every claim linked to its source page. Human reviewers keep final say on everything.

The outcome

First-review time fell by roughly 60%. Every extraction is audit-trailed for compliance. The credit team now spends its day on judgement, not on paperwork — and the eval harness catches quality drift before users do.

Healthcare Anonymised example

Returning hours per week to clinicians with assisted documentation.

The challenge

Across a network of clinics, clinicians were completing notes long after their last appointment — a burnout risk and a retention problem. Previous tooling attempts failed on trust: nobody wanted unreviewed AI text in the record.

What we did

We deployed a documentation assistant inside the network’s own environment. It drafts structured notes from consultations in the clinic’s templates; nothing enters the record without the clinician’s review and signature. We trained champions in each clinic and measured adoption weekly.

The outcome

Clinicians recovered several hours per week each; adoption passed 90% within two months because the workflow respected their sign-off. Governance praised the audit logging. The network now runs the system without us.

Manufacturing Anonymised example

Tripling quote throughput with an RFQ-to-quote agent.

The challenge

An industrial distributor received a constant stream of RFQs by email — every one manually parsed, matched against a large catalogue, and priced. Response time was the #1 complaint from their best customers.

What we did

In three sprints we built an agent that parses incoming RFQs, matches line items to the catalogue with confidence scores, drafts the quotation, and routes anything ambiguous to a human. Sales approves every quote before it leaves the building.

The outcome

Quote throughput roughly tripled and turnaround time nearly halved. Low-confidence matches route to people automatically, so quality held. The sales team describes the agent as “a colleague who does the boring half of the job.”

References available on request.

We’ll happily talk you through the unanonymised detail of relevant work — under NDA, with our clients’ permission — on a call.